AWS Machine Learning

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. Automating data labeling Data labeling is an inherently labor-intensive task that involves humans (labelers) to label the data.

Scripts 96

Scripts Construction Engineering APIs 96

AWS Machine Learning

MAY 5, 2023

By uploading a small set of training images, Amazon Rekognition automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. Lastly, we cover the data ingestion by an intelligent search service, powered by ML.

Engineering 102

Engineering APIs Scripts Enterprise 102

AWS Machine Learning

NOVEMBER 1, 2022

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. Amazon SageMaker Pipelines helps you automate the different steps of the ML lifecycle, including data preprocessing, training, tuning and evaluating ML models, and deploying them. SageMaker pipeline steps.

Scripts 78

Scripts APIs Metrics Engineering 78

AWS Machine Learning

DECEMBER 21, 2022

Today, companies are establishing feature stores to provide a central repository to scale ML development across business units and data science teams. The offline store is an append-only store and can be used to store and access historical feature data. Table formats provide a way to abstract data files as a table.

Scripts 73

Scripts Engineering APIs Big data 73

Totango

APRIL 26, 2021

Integration – Enhanced and easier control of the data flow from Totango to customer systems. . Download Campaign Metrics for Targeted Users. System Browser Scripts settings – You can now enable/disable session recordings in Global Settings -> General.

Scripts 62

Scripts APIs Customer retention Enterprise 62

AWS Machine Learning

NOVEMBER 21, 2022

Evaluate model performance on the hold-out test data with various evaluation metrics. Extract and analyze data from documents. We use Amazon Simple Storage Service (Amazon S3) alongside SageMaker to store the training data and model artifacts, and Amazon CloudWatch to log training and endpoint outputs. Fraud detection.

Scripts 71

Scripts Metrics APIs Healthcare 71

AWS Machine Learning

MARCH 1, 2023

In order to do so, customers have to retrain their existing entity extraction models with new training data that includes COVID. Custom Comprehend users need to manually monitor model performance to assess drifts, maintain data to retrain models, and select the right models that improve performance.

APIs 68

APIs Metrics Accountability Scripts 68

AWS Machine Learning

APRIL 19, 2023

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. The following diagram outlines the workflow of the DJL solution.

Engineering 75

Engineering APIs Scripts Education 75

AWS Machine Learning

NOVEMBER 21, 2023

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. To do this, we’ll use a predictive maintenance example where we classify faults in an operational pump that’s streaming live sensor data. We

Engineering 90

Engineering APIs Scripts Automotive 90

AWS Machine Learning

NOVEMBER 17, 2022

Beyond model accuracy, other potential metrics of importance are model training time and inference time. Given the iterative nature of ML model development, faster training times allow data scientists to quickly test various hypotheses. In this post, we use the API route, and present a notebook with various helper scripts.

APIs 79

APIs Scripts Metrics 79

AWS Machine Learning

NOVEMBER 16, 2022

Enhanced monitoring and observability for ML workloads including Amazon EKS, Kubeflow metrics, and application logs using Prometheus, Grafana, and Amazon CloudWatch integrations. Automated deployment options have been improved and simplified using Kustomize scripts and Helm charts. Kubeflow on AWS 1.6.1 and PyTorch 1.12.1.

Metrics 67

Metrics Scripts Enterprise APIs 67

AWS Machine Learning

MARCH 20, 2023

Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision data types like 8-bit integer (INT8) instead of the usual 32-bit floating point (FP32). Refer to the appendix for instance details and benchmark data.

Calibration 78

Calibration Scripts Benchmark APIs 78

AWS Machine Learning

MAY 22, 2023

The power of LLMs comes from their capacity to learn and generalize from extensive and diverse training data. Models can also be trained in a supervised fashion using labeled data to accomplish a set of tasks (for example, is this movie review positive, negative, or neutral). In addition to questions with answers, SQuAD2.0

APIs 92

APIs Scripts Metrics Accountability 92

AWS Machine Learning

NOVEMBER 16, 2022

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Upon a new model version registration, someone with the authority to approve the model based on the metrics should approve or reject the model.

Accountability 82

Accountability Best practices Scripts Engineering 82

Hodusoft

MAY 10, 2024

Metrics to Measure the Performance of Your Virtual Call Center Read More Benefits Provided by Virtual Call Centers Over Traditional Call Centers Compared to traditional call and contact centers, the benefits of virtual call and contact centers are many. VoIP uses the internet for transmission of audio and data, which reduces the call cost.

virtual call center 52

virtual call center Call Center Contact Center call center software 52

AWS Machine Learning

AUGUST 11, 2022

In real-world data, entities often involve rich relationships with other entities. Additionally, it’s challenging to construct a streaming data pipeline that can feed incoming events to a GNN real-time serving API. The first part is a pipeline that processes the data, trains GNN models, and deploys the trained models.

Construction 83

Construction APIs Accountability Scripts 83

AWS Machine Learning

AUGUST 3, 2022

For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console , AWS Command Line Interface , or Amazon Comprehend APIs. To train a custom entity recognizer, you can provide training data to Amazon Comprehend as annotations or entity lists.

Benchmark 67

Benchmark APIs Metrics Scripts 67

AWS Machine Learning

SEPTEMBER 1, 2023

Specifically, we briefly introduce MLOps principles and focus on the main differentiators compared to FMOps and LLMOps regarding processes, people, model selection and evaluation, data privacy, and model deployment. These data owners are focused on providing access to their data to multiple business units or teams.

Engineering 106

Engineering Accountability Construction APIs 106

AWS Machine Learning

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

Best practices 126

Best practices Government Engineering Accountability 126

AWS Machine Learning

AUGUST 14, 2023

However, we need to ensure that the LLMs limit the responses to company data, thereby mitigating model hallucinations. Some of the models offer capabilities for you to fine-tune them with your own data. as our example data to perform retrieval augmented question answering on.

Enterprise 69

Enterprise Scripts Best practices Analytics 69

aircall

JUNE 11, 2021

The 1960s — Cloud technology and data communications were invented, but these forms of telephony didn’t become mainstream until later. 2015 — An open API (applied programming interface) technology was invented to allow software applications to sync and share data between them. Telecommunication Now.

voip 62

voip APIs Call Center Telecommunications 62

NICE inContact

FEBRUARY 13, 2018

During an interaction, a screen pops up to the agent with relevant customer information, like their name, the selection they made in the IVR, their CRM profile, and maybe even some agent scripting. However, benefits extend to other contact center metrics as well. Average Handle Time.

Contact Center 49

Contact Center Average Handle Time Wait times APIs 49

AWS Machine Learning

FEBRUARY 1, 2023

Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged. One approach to moderate videos is to model video data as a sample of image frames and use image content moderation models to process the frames individually.

APIs 66

APIs Scripts Metrics Surveys 66

AWS Machine Learning

OCTOBER 10, 2023

Trained on 680 thousand hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. As a SageMaker JumpStart model hub customer, you can use ASR without having to maintain the model script outside of the SageMaker SDK.

APIs 94

APIs Scripts Metrics Accountability 94

The Northridge Group

MAY 2, 2023

To get a handle on ChatGPT, its implications, benefits, challenges, and best practices for contact centers we had a virtual conversation recently with Nathan Hart, Senior Director of Technology, Solutioning & Data Analytics, The Northridge Group. But ChatGPT can do much more than just reply.

Contact Center 67

Contact Center B2C Chatbots B2B 67

AWS Machine Learning

DECEMBER 6, 2022

By retraining the model with a combination of original training data and successful adversarial examples, the retrained model will be more robust against future attacks. The following charts illustrate our data distribution. compute metrics function def compute_metrics(pred): targets = 1 * (pred.label_ids >= 0.5)

Engineering 74

Engineering APIs Construction Metrics 74

AWS Machine Learning

JANUARY 8, 2024

However, they’re unable to gain insights such as using the information locked in the documents for large language models (LLMs) or search until they extract the text, forms, tables, and other structured data. In the following sections, we first describe the script solution, followed by the AWS CDK construct solution.

Scripts 91

Scripts Construction APIs Healthcare 91

AWS Machine Learning

NOVEMBER 9, 2022

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. It helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. The Python script starts the NGINX, Flask, and Triton server.

Engineering 79

Engineering Accountability APIs Metrics 79

AWS Machine Learning

AUGUST 11, 2022

The process of applying AI and machine learning (ML)-based solutions to find data anomalies involves a lot of complexity in ingesting, curating, and preparing data in the right format and then optimizing and maintaining the effectiveness of these ML models over long periods of time. Streaming data ingestion and storage.

Metrics 70

Metrics APIs Scripts Wireless 70

AWS Machine Learning

APRIL 29, 2024

This is a guest post co-authored with Ville Tuulos (Co-founder and CEO) and Eddie Mattia (Data Scientist) of Outerbounds. This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons. Metaflow’s coherent APIs simplify the process of building real-world ML/AI systems in teams.

APIs 102

APIs Engineering Scripts Management 102

AWS Machine Learning

FEBRUARY 19, 2024

Creators can also customize the model with their own data to output on-brand images in a specific style. It converts images and English text into semantic vectors, capturing meaning and relationships in your data. For Python scripts, you can use the AWS SDK for Python (Boto3). exclusive) to 10.0 Parse and decode the response.

Scripts 102

Scripts APIs Entertainment Engineering 102

Enghouse Interactive

OCTOBER 21, 2020

As noted in the 2019 Dimension Data Customer Experience (CX) Benchmarking report: 88% of contact center decision-makers expect self-service volumes to increase over the next 12 months. However, if you’re not sure about your data, or you don’t have any data, then measure first.

Self service 76

Self service Contact Center Benchmark Chatbots 76

AWS Machine Learning

APRIL 23, 2024

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

APIs 108

APIs Enterprise Scripts Accountability 108

AWS Machine Learning

SEPTEMBER 8, 2023

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

APIs 99

APIs Scripts Entertainment Personalization 99

JustCall

APRIL 12, 2023

JustCall vs Aircall Customer Reviews Comparison The product score mentioned here is a culmination of real-time data from verified user reviews. This section will enable you to make the most optimal choice based on your personalized business requirements.

Real estate 52

Real estate Interactive Voice Response CRM Call Center 52

AWS Machine Learning

DECEMBER 6, 2023

When prompted correctly, these models can carry coherent conversations without any task-specific training data. However, they can’t generalize well to enterprise-specific questions because, to generate an answer, they rely on the public data they were exposed to during pre-training.

Analytics 105

Analytics APIs Engineering Construction 105

AWS Machine Learning

SEPTEMBER 18, 2023

Data scientists have to address challenges like data partitioning, load balancing, fault tolerance, and scalability. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows.

Scripts 103

Scripts APIs Government Construction 103